Functional MRI-based lie detection: scientific and societal challenges

Behavioral dishonesty in multiscenes: Associations with trait honesty and neural patterns during (dis)honesty video-watching

Cross-situational inconsistency is common in the expression of honesty traits; yet, there is insufficient emphasis on behavioral dishonesty across multiple contexts. The current study aimed to investigate behavioral dishonesty in various contexts and reveal the associations between trait honesty, behavioral dishonesty, and neural patterns of observing others behave honestly or dishonestly in videos (abbr.: (dis)honesty video-watching). First, the results revealed limitations in using trait honesty to reflect variations in dishonest behaviors and predict behavioral dishonesty. The finding highlights the importance of considering neural patterns in understanding and predicting dishonest behaviors. Second, by comparing the predictive performance of seven types of data across three neural networks, the results showed that functional connectivity in the hypothesis-driven network during (dis)honesty video-watching provided the highest predictive power in predicting multitask behavioral dishonesty. Last, by applying the feature elimination method, the midline self-referential regions (medial prefrontal cortex, posterior cingulate cortex, and anterior cingulate cortex), anterior insula, and striatum were identified as the most informative brain regions in predicting behavioral dishonesty. In summary, the study offered insights into individual differences in deception and the intricate connections among trait honesty, behavioral dishonesty, and neural patterns during (dis)honesty video-watching.

To lie or to tell the truth? The influence of processing the opponent's feedback on the forthcoming choice

Introduction

The brain mechanisms of deceptive behavior are relatively well studied, and the key brain regions involved in its processing were established. At the same time, the brain mechanisms underlying the processes of preparation for deception are less known.

Methods

We studied BOLD-signal changes during the presentation of the opponent's feedback to a previous deceptive or honest action during the computer game. The goal of the game was to mislead the opponent either by means of deception or by means of telling the truth.

Results

As a result, it was shown that several brain regions that were previously demonstrated as involved in deception execution, such as the left anterior cingulate cortex and anterior insula, also underlie processes related to deception preparation.

Discussion

The results obtained also allowed us to suggest that brain regions responsible for performance monitoring, intention assessment, suppression of non-selected solutions, and reward processing could be involved in shaping future action selection and preparation for deception. By shedding light on the brain mechanisms underlying deception, our study contributes to a deeper understanding of this complex cognitive process. Furthermore, it emphasizes the significance of exploring brain mechanisms governing the choice between deception and truth at various stages of decision-making.

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods' efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

Detecting feigned cognitive impairment using pupillometry on the Warrington Recognition Memory Test for Words

OBJECTIVE

Pupillometry provides information about physiological and psychological processes related to cognitive load, familiarity, and deception, and it is outside of conscious control. This study examined pupillary dilation patterns during a performance validity test (PVT) among adults with true and feigned impairment of traumatic brain injury (TBI).

PARTICIPANTS AND METHODS

Participants were 214 adults in three groups: adults with bona fide moderate to severe TBI (TBI; n = 51), healthy comparisons instructed to perform their best (HC; n = 72), and healthy adults instructed and incentivized to simulate cognitive impairment due to TBI (SIM; n = 91). The Recognition Memory Test (RMT) was administered in the context of a comprehensive neuropsychological battery. Three pupillary indices were evaluated. Two pure pupil dilation (PD) indices assessed a simple measure of baseline arousal (PD-Baseline) and a nuanced measure of dynamic engagement (PD-Range). A pupillary-behavioral index was also evaluated. Dilation-response inconsistency (DRI) captured the frequency with which examinees displayed a pupillary familiarity response to the correct answer but selected the unfamiliar stimulus (incorrect answer).

RESULTS

All three indices differed significantly among the groups, with medium-to-large effect sizes. PD-Baseline appeared sensitive to oculomotor dysfunction due to TBI; adults with TBI displayed significantly lower chronic arousal as compared to the two groups of healthy adults (SIM, HC). Dynamic engagement (PD-Range) yielded a hierarchical structure such that SIM were more dynamically engaged than TBI followed by HC. As predicted, simulators engaged in DRI significantly more frequently than other groups. Moreover, subgroup analyses indicated that DRI differed significantly for simulators who scored in the invalid range on the RMT (n = 45) versus adults with genuine TBI who scored invalidly (n = 15).

CONCLUSIONS

The findings support continued research on the application of pupillometry to performance validity assessment: Overall, the findings highlight the promise of biometric indices in multimethod assessments of performance validity.

Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario

Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous lie detection model was used in the present study. We interviewed 10 healthy subjects to identify deception using the fNIRS system. A card game frequently referred to as a bluff or cheat was introduced. This game was selected because its rules are ideal for testing our hypotheses. The optical probe of the fNIRS was placed on the subject's forehead, and we acquired optical density signals, which were then converted into oxy-hemoglobin and deoxy-hemoglobin signals using the Modified Beer-Lambert law. The oxy-hemoglobin signal was preprocessed to eliminate noise. In this study, we proposed three artificial neural networks inspired by deep learning models, including AlexNet, ResNet, and GoogleNet, to classify deception and truth-telling. The proposed models achieved accuracies of 88.5%, 88.0%, and 90.0%, respectively. These proposed models were compared with other classification models, including k-nearest neighbor, linear support vector machines (SVM), quadratic SVM, cubic SVM, simple decision trees, and complex decision trees. These comparisons showed that the proposed models performed better than the other state-of-the-art methods.

Neural signatures of emotion regulation

Emotional experience is central to a fulfilling life. Although exposure to negative experiences is inevitable, an individual's emotion regulation response may buffer against psychopathology. Identification of neural activation patterns associated with emotion regulation via an fMRI task is a promising and non-invasive means of furthering our understanding of the how the brain engages with negative experiences. Prior work has applied multivariate pattern analysis to identify signatures of response to negative emotion-inducing images; we adapt these techniques to establish novel neural signatures associated with conscious efforts to modulate emotional response. We model voxel-level activation via LASSO principal components regression and linear discriminant analysis to predict if a subject was engaged in emotion regulation and to identify brain regions which define this emotion regulation signature. We train our models using 82 participants and evaluate them on a holdout sample of 40 participants, demonstrating an accuracy up to 82.5% across three classes. Our results suggest that emotion regulation produces a unique signature that is differentiable from passive viewing of negative and neutral imagery.

Minding Rights: Mapping Ethical and Legal Foundations of 'Neurorights'

The rise of neurotechnologies, especially in combination with artificial intelligence (AI)-based methods for brain data analytics, has given rise to concerns around the protection of mental privacy, mental integrity and cognitive liberty - often framed as "neurorights" in ethical, legal, and policy discussions. Several states are now looking at including neurorights into their constitutional legal frameworks, and international institutions and organizations, such as UNESCO and the Council of Europe, are taking an active interest in developing international policy and governance guidelines on this issue. However, in many discussions of neurorights the philosophical assumptions, ethical frames of reference and legal interpretation are either not made explicit or conflict with each other. The aim of this multidisciplinary work is to provide conceptual, ethical, and legal foundations that allow for facilitating a common minimalist conceptual understanding of mental privacy, mental integrity, and cognitive liberty to facilitate scholarly, legal, and policy discussions.

Building a second-opinion tool for classical polygraph

Classical polygraph screenings are routinely used by critical businesses such as banking, law enforcement agencies, and federal governments. A major concern of scientific communities is that screenings are prone to errors. However, screening errors are not only due to the method, but also due to human (polygraph examiner) error. Here we show application of machine learning (ML) to detect examiner errors. From an ML perspective, we trained an error detection model in the absence of labeled errors. From a practical perspective, we devised and tested successfully a second-opinion tool to find human errors in examiners' conclusions, thus reducing subjectivity of polygraph screenings. We report novel features that uplift the model's accuracy, and experimental results on whether people lie differently on different topics. We anticipate our results to be a step towards rethinking classical polygraph practices.

Verbal Lie Detection: Its Past, Present and Future

This article provides an overview of verbal lie detection research. This type of research began in the 1970s with examining the relationship between deception and specific words. We briefly review this initial research. In the late 1980s, Criteria-Based Content Analysis (CBCA) emerged, a veracity assessment tool containing a list of verbal criteria. This was followed by Reality Monitoring (RM) and Scientific Content Analysis (SCAN), two other veracity assessment tools that contain lists of verbal criteria. We discuss their contents, theoretical rationales, and ability to identify truths and lies. We also discuss similarities and differences between CBCA, RM, and SCAN. In the mid 2000s, 'Interviewing to deception' emerged, with the goal of developing specific interview protocols aimed at enhancing or eliciting verbal veracity cues. We outline the four most widely researched interview protocols to date: the Strategic Use of Evidence (SUE), Verifiability Approach (VA), Cognitive Credibility Assessment (CCA), and Reality Interviewing (RI). We briefly discuss the working of these protocols, their theoretical rationales and empirical support, as well as the similarities and differences between them. We conclude this article with elaborating on how neuroscientists can inform and improve verbal lie detection.

Tasks activating the default mode network map multiple functional systems

Recent developments in network neuroscience suggest reconsidering what we thought we knew about the default mode network (DMN). Although this network has always been seen as unitary and associated with the resting state, a new deconstructive line of research is pointing out that the DMN could be divided into multiple subsystems supporting different functions. By now, it is well known that the DMN is not only deactivated by tasks, but also involved in affective, mnestic, and social paradigms, among others. Nonetheless, it is starting to become clear that the array of activities in which it is involved, might also be extended to more extrinsic functions. The present meta-analytic study is meant to push this boundary a bit further. The BrainMap database was searched for all experimental paradigms activating the DMN, and their activation likelihood estimation maps were then computed. An additional map of task-induced deactivations was also created. A multidimensional scaling indicated that such maps could be arranged along an anatomo-psychological gradient, which goes from midline core activations, associated with the most internal functions, to that of lateral cortices, involved in more external tasks. Further multivariate investigations suggested that such extrinsic mode is especially related to reward, semantic, and emotional functions. However, an important finding was that the various activation maps were often different from the canonical representation of the resting-state DMN, sometimes overlapping with it only in some peripheral nodes, and including external regions such as the insula. Altogether, our findings suggest that the intrinsic-extrinsic opposition may be better understood in the form of a continuous scale, rather than a dichotomy.

Resting-state functional connectivity of social brain regions predicts motivated dishonesty

Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain regions. Recent work has built a link between brain networks in resting state to dishonesty in Western participants. To determine and reproduce the relevant neural patterns and build an interpretable model to predict dishonesty, we analyzed two conceptually similar datasets containing rsfMRI data with different dishonesty tasks. Both tasks implemented the information-passing paradigm, in which monetary rewards were employed to induce dishonesty. We applied connectome-based predictive modeling (CPM) to build a model among FC within and between four social brain networks (reward, self-referential, moral, and cognitive control). The CPM analysis indicated that FCs of social brain networks are predictive of dishonesty rate, especially FCs within reward network, and between self-referential and cognitive control networks. Our study offers an conceptual replication with integrated model to predict dishonesty with rsfMRI, and the results suggest that frequent motivated dishonest decisions may require the higher engagement of social brain regions.

[Cybersecurity of brain-computer interfaces]

Brain-computer interfaces inspire visions of superhuman powers, enabling users to control protheses and other devices solely with their thoughts. But the rapid development and commercialization of this technology also brings security risks. Attacks on brain-computer interfaces may cause harrowing consequences for users, from eavesdropping on neurological data to manipulating brain activity. At present, data protection law, the regulation of medical devices, and the new rules on the sale of goods with digital elements all govern aspects of cybersecurity. There are, nevertheless, significant gaps. The article analyzes how the legal system currently addresses the risks of cyberattacks on brain-computer interfaces-and how policymakers could address such risks in the future.

Detecting concealed information using functional near-infrared spectroscopy (fNIRS) combined with skin conductance, heart rate, and behavioral measures

In this study, brain imaging data from functional near-infrared spectroscopy (fNIRS) associated with skin conductance response (SCR), heart rate (HR), and reaction time (RT) were combined to determine if the combination of these indicators could improve the efficiency of deception detection in concealed information test (CIT). During the CIT, participants were presented with a series of names and cities that served as target, probe, or irrelevant stimuli. In the guilty group, the probe stimuli were the participants' own names and hometown cities, and they were asked to deny this information. Our results revealed that probe items were associated with longer RT, larger SCR, slower HR, and higher oxyhemoglobin (HbO) concentration changes in the inferior prefrontal gyrus (IFG), middle frontal gyrus (MFG), and the superior frontal gyrus (SFG) compared with irrelevant items for participants in the guilty group but not in the innocent group. Furthermore, our results suggested that the combination of RT, SCR, HR, and fNIRS indicators could improve the deception detection efficiency to a very high area under the ROC curve (0.94) compared with any of the single indicators (0.74-0.89). The improved deception detection efficiency might be attributed to the reduction of random error and the diversiform underlying the psychophysiological mechanisms reflected by each indicator. These findings demonstrate a feasible way to improve the deception detection efficiency by using combined multiple indicators.

Machine learning in neuroimaging: from research to clinical practice

Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.

Sabotage Detection Using DL Models on EEG Data From a Cognitive-Motor Integration Task

Objective clinical tools, including cognitive-motor integration (CMI) tasks, have the potential to improve concussion rehabilitation by helping to determine whether or not a concussion has occurred. In order to be useful, however, an individual must put forth their best effort. In this study, we have proposed a novel method to detect the difference in cortical activity between best effort (no-sabotage) and willful under-performance (sabotage) using a deep learning (DL) approach on the electroencephalogram (EEG) signals. The EEG signals from a wearable four-channel headband were acquired during a CMI task. Each participant completed sabotage and no-sabotage conditions in random order. A multi-channel convolutional neural network with long short term memory (CNN-LSTM) model with self-attention has been used to perform the time-series classification into sabotage and no-sabotage, by transforming the time-series into two-dimensional (2D) image-based scalogram representations. This approach allows the inspection of frequency-based, and temporal features of EEG, and the use of a multi-channel model facilitates in capturing correlation and causality between different EEG channels. By treating the 2D scalogram as an image, we show that the trained CNN-LSTM classifier based on automated visual analysis can achieve high levels of discrimination and an overall accuracy of 98.71% in case of intra-subject classification, as well as low false-positive rates. The average intra-subject accuracy obtained was 92.8%, and the average inter-subject accuracy was 86.15%. These results indicate that our proposed model performed well on the data of all subjects. We also compare the scalogram-based results with the results that we obtained by using raw time-series, showing that scalogram-based gave better performance. Our method can be applied in clinical applications such as baseline testing, assessing the current state of injury and recovery tracking and industrial applications like monitoring performance deterioration in workplaces.

Functional Interactions Between Neural Substrates of Socio-cognitive Mechanisms Involved in Simple Deception and Manipulative Truth

Introduction: Deceptive intentions may be realized by imparting false (simple deception) or true (manipulative truth) information. Both forms of deception require inferring others' thoughts and are underpinned by the theory of mind (TOM) neural system. Manipulative truth is thought to more strongly recruit these processes. However, the organization of functional interactions underlying simple deception and manipulative truth remains unclear. Materials and Methods: We performed psychophysiological interaction analysis for a key node in the TOM system, the right temporoparietal junction (rTPJ), using functional MRI data obtained from 23 volunteers (14 men and 9 women, age range 18-45 years) during the sender-receiver game. During the game, participants sent true, simple deceptive, or manipulative truthful messages to another player according to their own choice. A Bayesian approach to statistics was employed to perform statistical inference and define voxels with significant changes in functional interactions. Results: We observed functional interactions between nodes of the TOM system (bilateral TPJ, left precuneus, left dorsomedial prefrontal cortex, and right superior temporal sulcus) characterizing both forms of deception. We identified an increment in functional interactions of the rTPJ with the left TPJ (lTPJ) and right precuneus associated with manipulative truth. Furthermore, we demonstrated that a higher rate of manipulative truthful actions was associated with weaker functional interactions between the rTPJ and lTPJ, left precuneus, and left dorsomedial prefrontal cortex. Discussion: Compared with simple deception, manipulative truth is associated with a higher demand for socio-cognitive processes that contributes to the cognitive load of this form of deception.

Meta-analytic connectivity modelling of deception-related brain regions

Brain-based deception research began only two decades ago and has since included a wide variety of contexts and response modalities for deception paradigms. Investigations of this sort serve to better our neuroscientific and legal knowledge of the ways in which individuals deceive others. To this end, we conducted activation likelihood estimation (ALE) and meta-analytic connectivity modelling (MACM) using BrainMap software to examine 45 task-based fMRI brain activation studies on deception. An activation likelihood estimation comparing activations during deceptive versus honest behavior revealed 7 significant peak activation clusters (bilateral insula, left superior frontal gyrus, bilateral supramarginal gyrus, and bilateral medial frontal gyrus). Meta-analytic connectivity modelling revealed an interconnected network amongst the 7 regions comprising both unidirectional and bidirectional connections. Together with subsequent behavioral and paradigm decoding, these findings implicate the supramarginal gyrus as a key component for the sociocognitive process of deception.

Forensic Brain-Reading and Mental Privacy in European Human Rights Law: Foundations and Challenges

A central question in the current neurolegal and neuroethical literature is how brain-reading technologies could contribute to criminal justice. Some of these technologies have already been deployed within different criminal justice systems in Europe, including Slovenia, Italy, England and Wales, and the Netherlands, typically to determine guilt, legal responsibility, or recidivism risk. In this regard, the question arises whether brain-reading could permissibly be used against the person's will. To provide adequate legal protection from such non-consensual brain-reading in the European legal context, ethicists have called for the recognition of a novel fundamental legal right to mental privacy. In this paper, we explore whether these ethical calls for recognising a novel legal right to mental privacy are necessary in the European context. We argue that a right to mental privacy could be derived from, or at least developed within in the jurisprudence of the European Court of Human Rights, and that introducing an additional fundamental right to protect against (forensic) brain-reading is not necessary. What is required, however, is a specification of the implications of existing rights for particular neurotechnologies and purposes.

Functional connectivity between the caudate and medial prefrontal cortex reflects individual honesty variations in adults and children

Deception emerges in early childhood and prevails in adults. Activation patterns in previous adults' task-state functional magnetic resonance imaging (fMRI), though sensitive to state honesty on a specific decision, are less reliable reflecting trait honesty. Besides of state honesty, most previous neuroimaging studies about dishonesty suffer the generalization problem due to the major focus on adults with children unexplored. To investigate honesty associated functional brain networks variations, 98 healthy adults (Age: 18-28 y.o.; 49 males and 49 females) were invited to participate in a resting-state functional magnetic resonance imaging (rfMRI) study (Study 1). We investigated how functional connections between the caudate and the medial prefrontal cortex (mPFC) change among adults who differ in self-reported trait honesty. Results showed that adults with higher trait honesty have increased functional connectivity from the caudate to the mPFC, which is identified as an honesty-related hub region in global brain connectivity analysis and connects more tightly to a wide range of brain regions including the amygdala. Study 2 compared functional connectivity between children with high vs. low lying frequencies (Age: 6-16 y.o.; 61 males and 39 females) based on a publicly accessible database of rfMRI. Consistent with findings in adults, increased functional connectivity from the caudate to the mPFC was found in less frequently lying children. Despite different honesty indicators of self-reported honesty trait in adults and parent-reported lying patterns in children, consistent findings have been noted in the two samples with regards to functional connectivity variations between reward-related and self-related brain regions. These findings suggest functional connectivity alterations between the caudate and the mPFC contribute to honesty variations in both adults and children.

What Deception Tasks Used in the Lab Really Do: Systematic Review and Meta-analysis of Ecological Validity of fMRI Deception Tasks

Interpretation of the neural findings of deception without considering the ecological validity of the experimental tasks could lead to biased conclusions. In this study we classified the experimental tasks according to their inclusion of three essential components required for ecological validity: intention to lie, social interaction and motivation. First, we carried out a systematic review to categorize fMRI deception tasks and to weigh the degree of ecological validity of each one. Second, we performed a meta-analysis to identify if each type of task involves a different neural substrate and to distinguish the neurocognitive contribution of each component of ecological validity essential to deception. We detected six categories of deception tasks. Intention to lie was the component least frequently included, followed by social interaction. Monetary reward was the most frequent motivator. The results of the meta-analysis, including 59 contrasts, revealed that intention to lie is associated with activation in the left lateral occipital cortex (superior division) whereas the left angular gyrus and right inferior frontal gyrus (IFG) are engaged during lying under instructions. Additionally, the right IFG appears to participate in the social aspect of lying including simulated and real interactions. We found no effect of monetary reward in our analysis. Finally, tasks with high ecological validity recruited fewer brain areas (right insular cortex and bilateral anterior cingulate cortex (ACC)) compared to less ecological tasks, perhaps because they are more natural and realistic, and engage a wide network of brain mechanisms, as opposed to specific tasks that demand more centralized processes.

Detecting simulated versus bona fide traumatic brain injury using pupillometry

Objective: Pupil dilation patterns are outside of conscious control and provide information regarding neuropsychological processes related to deception, cognitive effort, and familiarity. This study examined the incremental utility of pupillometry on the Test of Memory Malingering (TOMM) in classifying individuals with verified traumatic brain injury (TBI), individuals simulating TBI, and healthy comparisons. Method: Participants were 177 adults across three groups: verified TBI (n = 53), feigned cognitive impairment due to TBI (SIM, n = 52), and heathy comparisons (HC, n = 72). Results: Logistic regression and ROC curve analyses identified several pupil indices that discriminated the groups. Pupillometry discriminated best for the comparison of greatest clinical interest, verified TBI versus simulators, adding information beyond traditional accuracy scores. Simulators showed evidence of greater cognitive load than both groups instructed to perform at their best ability (HC and TBI). Additionally, the typically robust phenomenon of dilating to familiar stimuli was relatively diminished among TBI simulators compared to TBI and HC. This finding may reflect competing, interfering effects of cognitive effort that are frequently observed in pupillary reactivity during deception. However, the familiarity effect appeared on nearly half the trials for SIM participants. Among those trials evidencing the familiarity response, selection of the unfamiliar stimulus (i.e., dilation-response inconsistency) was associated with a sizeable increase in likelihood of being a simulator. Conclusions: Taken together, these findings provide strong support for multimethod assessment: adding unique performance assessments such as biometrics to standard accuracy scores. Continued study of pupillometry will enhance the identification of simulators who are not detected by traditional performance validity test scoring metrics. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Role of Reward System in Dishonest Behavior: A Functional Near-Infrared Spectroscopy Study

Previous studies showed that the cortical reward system plays an important role in deceptive behavior. However, how the reward system activates during the whole course of dishonest behavior and how it affects dishonest decisions remain unclear. The current study investigated these questions. One hundred and two participants were included in the final analysis. They completed two tasks: monetary incentive delay (MID) task and an honesty task. The MID task served as the localizer task and the honesty task was used to measure participants' deceptive behaviors. Participants' spontaneous responses in the honesty task were categorized into three conditions: Correct-Truth condition (tell the truth after guessing correctly), Incorrect-Truth condition (tell the truth after guessing incorrectly), and Incorrect-Lie condition (tell lies after guessing incorrectly). To reduce contamination from neighboring functional regions as well as to increase sensitivity to small effects (Powell et al., Devel Sci 21:e12595, 2018), we adopted the individual functional channel of interest (fCOI) approach to analyze the data. Specially, we identified the channels of interest in the MID task in individual participants and then applied them to the honesty task. The result suggested that the reward system showed different activation patterns during different phases: In the pre-decision phase, the reward system was activated with the winning of the reward. During the decision and feedback phase, the reward system was activated when people made the decisions to be dishonest and when they evaluated the outcome of their decisions. Furthermore, the result showed that neural activity of the reward system toward the outcome of their decision was related to subsequent dishonest behaviors. Thus, the present study confirmed the important role of the reward system in deception. These results can also shed light on how one could use neuroimaging techniques to perform lie-detection.

Truth and lies in your eyes: Pupil dilation of White participants in truthful and deceptive responses to White and Black partners

In the present study, we examined the pupillary response of White participants who were asked to tell the truth or lie to White or Black partners. Research on cues to deception has assumed that lying is more cognitively demanding that truth telling. In line with this assumption, previous studies have shown that lying is associated with greater pupil dilation, a behavioral cue that typically manifests itself under conditions of stress or cognitive effort. In accordance with these results, we predicted greater pupil dilation when lying than when telling the truth. Furthermore, pupil dilation was expected to be greater when responding to White than Black partners. Finally, we hypothesized that pupil dilation would be greater when lying to White than Black partners. Participants were instructed to answer a set of questions, half truthfully and half deceptively. They were led to believe that White vs. Black partners (one male and one female) would ask the questions via computer connection. Indeed, we used feminine and masculine synthetic voices. Pupil dilation was assessed with a remote eye-tracking system. Results provided support for the first two hypotheses. However, the predicted interaction between race of partners and truth status of message (lying vs. telling the truth) was nonsignificant. Our findings highlight the importance of considering race in the study of truthful and deceptive communications.

Neural mechanisms of deception in a social context: an fMRI replication study

Deception is a form of manipulation aimed at misleading another person by conveying false or truthful messages. Manipulative truthful statements could be considered as sophisticated deception and elicit an increased cognitive load. However, only one fMRI study reported its neural correlates. To provide independent evidence for sophisticated deception, we carried out an fMRI study replicating the experimental paradigm and Bayesian statistical approach utilized in that study. During the experiment, participants played a game against an opponent by sending deliberate deceptive or honest messages. Compared to truth-telling, deceptive intentions, regardless of how they were fulfilled, were associated with increased BOLD signals in the bilateral temporoparietal junction (TPJ), left precuneus, and right superior temporal sulcus (STS). The right TPJ participates in the attribution of mental states, acting in a social context, and moral behaviour. Moreover, the other revealed brain areas have been considered nodes in the theory of mind brain neural system. Therefore, the obtained results reflect an increased demand for socio‑cognitive processes associated with deceptive intentions. We replicated the original study showing the involvement of the right TPJ and expanded upon it by revealing the involvement of the left TPJ, left precuneus and right STS in actions with deceptive intentions.

Both High Cognitive Load and Transcranial Direct Current Stimulation Over the Right Inferior Frontal Cortex Make Truth and Lie Responses More Similar

Deception scholars have argued that increasing the liar’s cognitive system artificially can produce deception cues. However, if too much load is imposed, the truth tellers’ performance can also be impaired. To address this issue, we designed a veracity task that incorporated a secondary task to increase cognitive load gradually. Also, because deception has been associated with activity in the inferior frontal cortex (IFC), we examined the influence of transcranial direct current stimulation (tDCS) of the IFC on performance. During stimulation, participants truthfully or deceptively indicated whether each of a number of statements shown on screen was true or not. Higher load decreased recall but not general compliance or response times (RTs). Truthful trials yielded higher compliance rates and faster RTs than deceptive trials except for the highest load level. Anodal right stimulation decreased compliance in truthful trials when participants were not overloaded. Truth telling was more vulnerable to cognitive load and tDCS than lying.

Neural correlates of anxiety under interrogation in guilt or innocence contexts

Interrogation elicits anxiety in individuals under scrutiny regardless of their innocence, and thus, anxious responses to interrogation should be differentiated from deceptive behavior in practical lie detection settings. Despite its importance, not many empirical studies have yet been done to separate the effects of interrogation from the acts of lying or guilt state. The present fMRI study attempted to identify neural substrates of anxious responses under interrogation in either innocent or guilt contexts by developing a modified "Doubt" game. Participants in the guilt condition showed higher brain activations in the right central-executive network and bilateral basal ganglia. Regardless of the person's innocence, we observed higher activation of the salience, theory of mind and sensory-motor networks-areas associated with anxiety-related responses in the interrogative condition, compared to the waived conditions. We further explored two different types of anxious responses under interrogation-true detection anxiety in the guilty (true positive) and false detection anxiety in the innocent (false positive). Differential neural responses across these two conditions were captured at the caudate, thalamus, ventral anterior cingulate and ventromedial prefrontal cortex. We conclude that anxiety is a common neural response to interrogation, regardless of an individual's innocence, and that there are detectable differences in neural responses for true positive and false positive anxious responses under interrogation. The results of our study highlight a need to isolate complex cognitive processes involved in the deceptive acts from the emotional and regulatory responses to interrogation in lie detection schemes.

Neural responses of in-group "favoritism" and out-group "discrimination" toward moral behaviors

People hate being deceived. However, what would it be if lies come from in-group members compared with that from out-group members? In the current Electroencephalography (EEG) study, we recruited thirty-six participants to play a modified estimator and advisor game to investigate the mental and neural processes to lies and truth conveyed by in-group and out-group members. At the behavioral level, lies are less morally acceptable, arose less positive emotion, and made participants distribute less money to the advisor in a dictator game. Meanwhile, participants liked the in-group university more than the out-group university and they thought they were more similar to in-group members than to out-group members. However, there were no significant interactions of group type (i.e., in-group and out-group) and message type (i.e., lies and truth) in the aforementioned behavioral assessments. At the neural level, significant interaction effects were found in the parietal N1 and P3 amplitude. More importantly, no significant N1 and P3 amplitude differences between in-group lies and truth were found, while outgroup lies elicited larger P3 amplitude than outgroup truth and out-group truth elicited larger N1 amplitude than outgroup lies. What's more, P3 amplitude differences between lies vs. truth positively correlated with fairness scores only in the in-group condition but not in the out-group condition. Current study showed that the P3 component was sensitive in capturing subtle differences when participants were processing different types of lies and truth that could not be captured by behavioral measurements. Besides, the fairness trait modulated the in-group bias related P3 patterns. The current study provides insight into the neurobiological mechanism underlying the mental process of in-group and out-group lies and truth, and suggests individuals' tendency of general in-group favoritism and out-group discrimination toward moral behaviors.

Interview based connectivity analysis of EEG in order to detect deception

Deception is mentioned as an expression or action which hides the truth and deception detection as a concept to uncover the truth. In this research, a connectivity analysis of Electro Encephalography study is presented regarding cognitive processes of an instructed liar/truth-teller about identity during an interview. In this survey, connectivity analysis is applied because it can provide unique information about brain activity patterns of lying and interaction among brain regions. The novelty of this paper lies in applying an open-ended questions interview protocol during EEG recording. We recruited 40 healthy participants to record EEG signal during the interview. For each subject, whole-brain functional and effective connectivity networks such as coherence, generalized partial direct coherence and directed directed transfer function, are constructed for the lie-telling and truth-telling conditions. The classification results demonstrate that lying could be differentiated from truth-telling with an accuracy of 86.25% with the leave-one-person-out method. Results show functional and effective connectivity patterns of lying for the average of all frequency bands are different in regions from that of truth-telling. The current study may shed new light on neural patterns of deception from connectivity analysis view point.

Increased Motor Cortex Excitability for Concealed Visual Information

Abstract. Deceptive behavior involves complex neural processes involving the primary motor cortex. The dynamics of this motor cortex excitability prior to lying are still not well understood. We sought to examine whether corticospinal excitability can be used to suggest the presence of deliberately concealed information in a modified version of the guilty knowledge test (GKT). Participants pressed keys to either truthfully or deceitfully indicate their familiarity with a series of faces. Motor-evoked potentials (MEPs) were recorded during response preparation to measure muscle-specific neural excitability. We hypothesized that MEPs would increase during the deceptive condition not only in the lie-telling finger but also in the suppressed truth-telling finger. We report a group-level increase in overall corticospinal excitability 300 ms following stimulus onset during the deceptive condition, without specific activation of the neural representation of the truth-telling finger. We discuss cognitive processes, particularly response conflict and/or automated responses to familiar stimuli, which may drive the observed nonspecific increase of motor excitability in deception.

Neuroscience and mental state issues in forensic assessment

Neuroscience has already changed how the law understands an individual's cognitive processes, how those processes shape behavior, and how bio-psychosocial history and neurodevelopmental approaches provide information, which is critical to understanding mental states underlying behavior, including criminal behavior. In this paper, we briefly review the state of forensic assessment of mental conditions in the relative culpability of criminal defendants, focused primarily on the weaknesses of current approaches. We then turn to focus on neuroscience approaches and how they have the potential to improve assessment, but with significant risks and limitations.

Using Polygraph to Detect Passengers Carrying Illegal Items

The present study examined the effectiveness of a Modified-Comparison Questions Technique, used in conjunction with the polygraph, to differentiate between common travelers, drug traffickers, and terrorists at transportation hubs. Two experiments were conducted using a mock crime paradigm. In Experiment 1, we randomly assigned 78 participants to either a drug condition, where they packed and lied about illicit drugs in their luggage, or a control condition, where they did not pack or lie about any illegal items. In Experiment 2, we randomly assigned 164 participants to one of the two conditions in Experiment 1 or an additional bomb condition, where they packed and lied about a bomb in their luggage. For both experiments, we assessed participants’ RR interval, heart rate, peak-to-peak amplitude of Galvanic Skin Response (GSR) and all three combined, using Discriminant Analyses to determine the classification accuracy of participants in each condition. In both experiments, we found decelerated heart rates and increased peak-to-peak amplitude of GSR in guilty participants when lying in response to questions regarding their crime. We also found accurate classifications of participants, in both Experiment 1 (drug vs. control: 84.2% vs. 82.5%) and Experiment 2 (drug vs. control: 82:1% vs. 95.1%; bomb vs. control: 93.2% vs. 95.1%; drug vs. bomb: 92.3% vs. 90.9%), above chance level. These findings indicate that Modified-CQT, combined with a polygraph test, is a viable method for investigating suspects of drug trafficking and terrorism at transportation hubs such as train stations and airports.

I lie, why don't you: Neural mechanisms of individual differences in self-serving lying

People tend to lie in varying degrees. To advance our understanding of the underlying neural mechanisms of this heterogeneity, we investigated individual differences in self-serving lying. We performed a functional magnetic resonance imaging study in 37 participants and introduced a color-reporting game where lying about the color would in general lead to higher monetary payoffs but would also be punished if get caught. At the behavioral level, individuals lied to different extents. Besides, individuals who are more dishonest showed shorter lying response time, whereas no significant correlation was found between truth-telling response time and the degree of dishonesty. At the neural level, the left caudate, ventromedial prefrontal cortex (vmPFC), right inferior frontal gyrus (IFG), and left dorsolateral prefrontal cortex (dlPFC) were key regions reflecting individual differences in making dishonest decisions. The dishonesty associated activity in these regions decreased with increased dishonesty. Subsequent generalized psychophysiological interaction analyses showed that individual differences in self-serving lying were associated with the functional connectivity among the caudate, vmPFC, IFG, and dlPFC. More importantly, regardless of the decision types, the neural patterns of the left caudate and vmPFC during the decision-making phase could be used to predict individual degrees of dishonesty. The present study demonstrated that lying decisions differ substantially from person to person in the functional connectivity and neural activation patterns which can be used to predict individual degrees of dishonesty.

Deconstructing multivariate decoding for the study of brain function

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function.

Insights From fMRI Studies Into Ingroup Bias

Intergroup biases can manifest themselves between a wide variety of different groups such as people from different races, nations, ethnicities, political or religious beliefs, opposing sport teams or even arbitrary groups. In this review we provide a neuroscientific overview of functional Magnetic Resonance Imaging (fMRI) studies that have revealed how group dynamics impact on various cognitive and emotional systems at different levels of information processing. We first describe how people can perceive the faces, words and actions of ingroup and outgroup members in a biased way. Second, we focus on how activity in brain areas involved in empathizing with the pain of others, such as the dorsal anterior cingulate cortex (dACC) and anterior insula (AI), are influenced by group membership. Third, we describe how group membership influences activity in brain areas involved in mentalizing such as the medial prefrontal cortex (mPFC) and temporoparietal junction (TPJ). Fourth, we discuss the involvement of the lateral orbitofrontal cortex (lOFC) in increased moral sensitivity for outgroup threats. Finally, we discuss how brain areas involved in the reward system such as the striatum and medial orbitofrontal cortex (mOFC), are more active when experiencing schadenfreude for outgroup harm and when rewarding ingroup (versus outgroup) members. The value of these neuroscientific insights to better understand ingroup bias are discussed, as well as limitations and future research directions.

Telling a truth to deceive: Examining executive control and reward-related processes underlying interpersonal deception

Does deception necessarily involve false statements that are incompatible with the truth? In some cases, people choose truthful statements in order to mislead others. This type of deception has been investigated less. The current study employed event-related brain potentials (ERPs) to investigate the neurocognitive processes when both truthful and false statements were used to deceive others. We focused our ERP analysis on two stages: a decision making stage during which participants decided whether to tell a false or a truthful statement, and an outcome evaluation stage during which participants evaluated whether their deception had succeeded or not. Results showed that in the decision making stage, intentions to deceive elicited larger N200s and smaller P300s than an honest control condition. During the outcome evaluation stage, success/failure feedback in the deception condition elicited larger Reward positivity (RewP) and feedback-P300 than feedback after honest responses. Importantly, whether participants chose to tell false or true statements, did not further modulate executive control or reward-related processes. Taken together, these results suggest that during interpersonal deception, having deceptive intentions engages executive control and reward-related processes regardless of the veracity of statements.

Learning to Detect Deception from Evasive Answers and Inconsistencies across Repeated Interviews: A Study with Lay Respondents and Police Officers

Previous research has shown that inconsistencies across repeated interviews do not indicate deception because liars deliberately tend to repeat the same story. However, when a strategic interview approach that makes it difficult for liars to use the repeat strategy is used, both consistency and evasive answers differ significantly between truth tellers and liars, and statistical software (binary logistic regression analyses) can reach high classification rates (Masip et al., 2016b). Yet, if the interview procedure is to be used in applied settings the decision process will be made by humans, not statistical software. To address this issue, in the current study, 475 college students (Experiment 1) and 142 police officers (Experiment 2) were instructed to code and use consistency, evasive answers, or a combination or both before judging the veracity of Masip et al.'s (2016b) interview transcripts. Accuracy rates were high (60% to over 90%). Evasive answers yielded higher rates than consistency, and the combination of both these cues produced the highest accuracy rates in identifying both truthful and deceptive statements. Uninstructed participants performed fairly well (around 75% accuracy), apparently because they spontaneously used consistency and evasive answers. The pattern of results was the same among students, all officers, and veteran officers only, and shows that inconsistencies between interviews and evasive answers reveal deception when a strategic interview approach that hinders the repeat strategy is used.

Delta plots do not reveal response inhibition in lying

The role of response inhibition in lying is debated. By using the delta-plot method applied to the Sheffield Lie Test, Debey, Ridderinkhof, De Houwer, De Schryver, and Verschuere (2015) provided evidence supporting the role of inhibition in lying. In the study of Debey et al., inhibitory skill was measured in terms of the size of the lie effect. However, to provide convincing evidence that delta plots highlight the role of response inhibition in lying, inhibitory ability must be evaluated independently from the size of the lie effect. After replicating original findings, this article shows that a delta plot analysis does not differentiate individuals with different inhibitory abilities, when inhibitory skill is measured by means of the Stop Signal Task, instead of the size of the lie effect. This suggests that researchers should be cautious when making conclusions about cognitive mechanisms based on the sole analysis of delta plots.

Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis

The present study examined how different brain regions interact with each other during spontaneous honest vs. dishonest communication. More specifically, we took a complex network approach based on the graph-theory to analyze neural response data when children are spontaneously engaged in honest or dishonest acts. Fifty-nine right-handed children between 7 and 12 years of age participated in the study. They lied or told the truth out of their own volition. We found that lying decreased both the global and local efficiencies of children’s functional neural network. This finding, for the first time, suggests that lying disrupts the efficiency of children’s cortical network functioning. Further, it suggests that the graph theory based network analysis is a viable approach to study the neural development of deception.

Lie construction affects information storage under high memory load condition

Previous studies indicate that lying consumes cognitive resources, especially working memory (WM) resources. Considering the dual functions that WM might play in lying: holding the truth-related information and turning the truth into lies, the present study examined the relationship between the information storage and processing in the lie construction. To achieve that goal, a deception task based on the old/new recognition paradigm was designed, which could manipulate two levels of WM load (low-load task using 4 items and high-load task using 6 items) during the deception process. The analyses based on the amplitude of the contralateral delay activity (CDA), a proved index of the number of representations being held in WM, showed that the CDA amplitude was lower in the deception process than that in the truth telling process under the high-load condition. In contrast, under the low-load condition, no CDA difference was found between the deception and truth telling processes. Therefore, we deduced that the lie construction and information storage compete for WM resources; when the available WM resources cannot meet this cognitive demand, the WM resources occupied by the information storage would be consumed by the lie construction.

The good lies: Altruistic goals modulate processing of deception in the anterior insula

When it comes to lies, the beneficiaries of one's dishonesty play an important role in the decision-making process. Altruistic lies that are made with the intention of benefiting others are a specific type of lies and very common in real life. While it has been shown that altruistic goals influence (dis)honest behaviors, the neural substrates of this effect is still unknown. To reveal how the brain integrates altruistic goals into (dis)honest decisions, this study used functional magnetic resonance imaging to examine the neural activity of participants in a real incentivized context while they were making (dis)honest decisions. We manipulated the beneficiaries of individuals' decisions (self vs. a charity) and whether the choices of higher payoffs involved deception or not. While finding that participants lied more often to benefit charities than for themselves, we observed that the altruistic goal of benefiting a charity, compared with the self-serving goal, reduced the activity in the anterior insula (AI) when lying to achieve higher payoffs. Furthermore, the degree of altruistic goal-induced reduction of AI activity was positively correlated with the degree of altruistic goal-induced reduction of honesty concerns. These results suggest that the AI serves as a neural hub in modulating the effect of altruistic goals on deception, which shed light on the underlying neural mechanism of altruistic lies. Hum Brain Mapp 38:3675-3690, 2017. © 2017 Wiley Periodicals, Inc.